Abstract
Epidemic forecasting has garnered increasing interest in the last decade, nurtured and scaffolded by various forecasting challenges organized by groups within the US federal government, including the Centers for Disease Control and Prevention (CDC) (1⇓–3), Office of Science and Technology Policy (OSTP) (4), and Defense Advanced Research Projects Agency (DARPA) (5), and elsewhere (6, 7). In 2017, after several years of experimentation with flu forecasting in academic groups, the CDC decided to incorporate influenza forecasting into its normal operations, including weekly public communications (8) and briefing to higher-ups. To provide more reliable infrastructure and support for its forecasting needs, the CDC in 2019 designated two national Centers of Excellence for Influenza Forecasting, one at the University of Massachusetts at Amherst (https://reichlab.io/people) and one at Carnegie Mellon University (https://delphi.cmu.edu/about/center-of-excellence/). Not unrelatedly, the last decade has also seen a rise in the importance of digital surveillance streams in public health, with improving epidemic tracking and forecasting models being a key application of these data. Digital streams, such as search and social media trends, have constituted a large part of the focus (9⇓⇓⇓⇓–14); however, even more broadly, data from auxiliary streams that operate outside of traditional public health reporting, such as online surveys, medical devices, or electronic medical records (EMRs), have received considerable attention as well (15⇓⇓⇓⇓⇓⇓⇓⇓⇓–25). The Carnegie Mellon Delphi group, which the two of us colead, has worked in both of these emerging disciplines—epidemic forecasting and building relevant auxiliary signals to aid such forecasting models—since 2012. In 2020, as the pandemic broke out, we struggled like many other groups to find ways to contribute to the national efforts to respond to the pandemic. We ended up shifting our … [↵][1]1To whom correspondence may be addressed. Email: ryantibs{at}cmu.edu. [1]: #xref-corresp-1-1
Highlights
Not unrelatedly, the last decade has seen a rise in the importance of digital surveillance streams in public health, with improving epidemic tracking and forecasting models being a key application of these data
Epidemic forecasting has garnered increasing interest in the last decade, nurtured and scaffolded by various forecasting challenges organized by groups within the US federal government, including the Centers for Disease Control and Prevention (CDC) [1,2,3], Office of Science and Technology Policy (OSTP) [4], and Defense Advanced Research Projects Agency (DARPA) [5], and elsewhere [6, 7]
The last decade has seen a rise in the importance of digital surveillance streams in public health, with improving epidemic tracking and forecasting models being a key application of these data
Summary
Is a very brief summary of the papers in this collection. 1) Reinhart et al [26] describe our group’s (ongoing) effort in building and maintaining COVIDcast: an open repository of real-time, geographically detailed COVID-19 indicators in the United States. 1) Reinhart et al [26] describe our group’s (ongoing) effort in building and maintaining COVIDcast: an open repository of real-time, geographically detailed COVID-19 indicators in the United States These indicators (a term we use interchangeably with signals) are derived from a diverse set of data sources: medical testing devices, medical insurance claims, internet search trends, appbased mobility data, and online surveys among others. 3) Salomon et al [28] focus on the US CTIS, an (ongoing) online survey operated by our group in partnership with Facebook This is a very rich source of data about the pandemic and its effect on people, only partially reflected by the indicators (derived from the survey) in the COVIDcast repository; the full dataset of individual, anonymized survey responses is available to researchers under a data use agreement. The paper presents analyses that reflect some basic and important characteristics of the international survey, reflecting its value abroad, where public health reporting efforts may be more limited than those in the US
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